profiles: restricted profiles for ordinary users, and a role gate that holds ...

`is_admin_only` was checked in one place out of nine and was not read from
config.json at all, so all seven profiles were reachable by every account with
role `user`. The flag now lives in config.json — the file is the baseline, a
`profile_overrides` row still wins on top of it — and one predicate,
`admin_only_blocked`, is the single place the rule is expressed. The nine
surfaces that list, switch to, spawn or resolve a profile all consult it:
`POST /sessions`, the WebSocket, switch_profile, list_profiles, the system
prompt's "Available profiles" block, spawn_agent and the Synapse reaction
runner. The prompt cache is now keyed by (profile, role), so a user's prompt
can never be served an admin's profile list.

The seven existing profiles (developer, discuss, dispatcher, modeler_3d,
navi_code, secretary, server_admin) are marked admin-only. Three new ones take
their place for ordinary users: assistant, designer_3d and coder. They share one
native tool set — ssh_exec, peer, reload_tools, create_mcp_server, test_mcp_tool,
image_view and gmail are withheld — and differ only in system prompt, model and
MCP groups. navi-web's raw `request` group, and the whole of gnexus-creds and
tgclient, are withheld too.

MCP per-user keys gain the missing half of the rule: a server that declares a
`user_key` slot is refused to anyone but an admin who has no personal key, and
is left out of their tool list entirely, instead of quietly falling back to the
owner's credential and appearing as a tool that cannot work. The refusal names
the server and points at Settings.

Also closes `GET /agents/prompts`, which served every profile's system prompt to
anyone, with no user dependency at all.

The accepted residual risk is written down in docs/profiles.md: the working
directory is a convention, not a sandbox.

synapse reactions W2: hidden dispatcher profile + synapse_instructions tool

agent parallelism: background tools, bg spawn_agent, parallel tool batches, message queue ...

- backgroundable tools (terminal/ssh_exec/peer/spawn_agent/code_exec) detach
  via TaskManager with per-session/global/spawn caps, rate limit and TTL
- completion delivery both ways: task_update event (out-of-band) + pending
  notes injected into the next turn; tasks tool (list/check/wait/cancel)
- parallel tool-call batches (profile-level gate, off by default) with
  ToolStarted up-front, tagged event mux, call-order results, one save,
  plus dangling tool_call repair on session load
- user message queue instead of busy error: message_queued frame,
  back-to-back drain on the same socket, headless fallback on disconnect
- pin mcp<2 (v2 renames FastMCP with breaking API changes)
- docs: tasks.md (new), websocket/api/agent/config updates, tasks manual,
  spawn_agent background param, persona contract section

profiles: retire gate flags and retrain prompts for the plan tool ...

- drop planning_enabled / planning_mandatory / planning_phase2_enabled /
  observe_skips_plan_enabled / adaptive_replan_enabled from AgentProfile,
  the loader and admin serialization
- add `plan` to native tools of developer, navi_code, tool_developer,
  secretary, server_admin, modeler_3d — without the gate they would
  otherwise lose planning entirely
- system prompts: call plan for non-trivial multi-step work before
  execution; complex plans get presented for confirmation; stuck or goal
  changed -> plan with a reason

Автономность мелких моделей: OUTPUT DISCIPLINE, milestone-todo, перехват финала хода ...

Два последовательных захода над одной проблемой — мелкие модели (12–30B) в navi_code
теряют автономность на трудных шагах: «остановился поболтать» вместо действия и
слишком большое расстояние между пунктами плана.

Заход 1 (З1–З3):
- З1: OUTPUT DISCIPLINE в системном промпте navi_code — «act, don't announce»,
  без few-shot антипримеров. Контракт хода: ответ без tool_calls = конец хода, поэтому
  объявление намерения текстом убивает автономность; правила заставляют вызывать
  инструмент в том же ходе.
- З2: плоский todo + метка группы milestone + декомпозиция. _parse_plan_steps
  возвращает list[tuple[milestone, text]]; milestone — метка группировки (не сущность,
  без статуса), «done» вычисляется при рендеринге; подшаги = больше плоских шагов
  (без вложенности). TUI side-panel группирует по milestone (плоский фолбэк при пустом
  milestone). Plan depth: max 15→20 + правило декомпозиции.
- З3: adaptive re-plan «длинный шаг» — nudge «разбей шаг» при in_progress ≥ порога
  итераций без смены todo (порог 4, раньше общего anti-stall warning на 8).

Заход 2 (шаги 1–3, после cloud-теста 31b vs 12b):
Корневая структурная причина: весь спасательный механизм (anti-stall warning с явным
предложением reflect, adaptive re-plan) живёт только внутри tool-цикла — nudge
инжектируется в pre_turn *следующей* итерации, которой при «остановился поболтать»
нет (ход закрылся по return до post_turn). 31b застревала через «продолжаю
tool-итерации» → дожала до warning → спаслась; 12b — через «замолчала текстом» → мимо
всех nudge.

- Шаг 1: перехват финала хода. Если модель выдала bare-text, но в todo есть открытые
  шаги (pending/in_progress) и лимит не исчерпан — НЕ эмитить StreamEnd, а сохранить
  ассистентский текст в session.context, поставить системный nudge и continue
  (без StreamEnd, без workers — консистентно с multi-iteration tool-турами). Счётчик
  final_interceptions на AgentTurnContext, лимит final_intercept_limit (default 2),
  эскалация жёсткости (мягкий → «second stop»). has_open_steps в todo.py: пустой
  todo → False (защита casual-сообщений), failed/skipped терминальны. Профильные
  флаги final_intercept_enabled/limit в base.py + loader + admin.
- Шаг 2: жёсткий reflect-триггер в промпте — «~3 tool attempts on the same step
  without progress → call reflect IN THIS TURN (tool call, not reasoning aloud)».
- Шаг 3: открыть replan для застревания — «call replan when reflect showed the whole
  approach is dead (not one failed step, but the approach itself)».

Тесты: 874 passed, 1 skipped. Новые — has_open_steps (5), final intercept (5),
milestone-группировка, adaptive long-step nudge, парсер шагов с milestone-маркером.

cloud: navi_code model → gemma4:31b-cloud для тестирования догадки (31b признала
застревание, 12b — нет); .env cloud-host уже gitignored.

Co-Authored-By: Claude <noreply@anthropic.com>

agent: bounded autonomy — scope boundary + observe-vs-act ...

navi_code had unwanted "free flight": an observe request ("look at a
directory") triggered the full Phase 3 plan with milestones + auto-todo,
and goal_anchoring then drove the agent to finish those steps, climbing
into sibling projects and executing milestone docs it found.

Two toggleable, default-off profile flags (on for navi_code):

- scope_boundary_enabled: injects a standing system message keeping the
  agent within the literally requested scope; forbids acting on
  discovered TODO/roadmap/milestone docs (report only).
- observe_skips_plan_enabled: Phase 1 classifies MODE: observe|act; an
  observe request skips Phase 2/3 — no multi-step plan, no auto-todo, no
  "execute step by step" prompt. The agent just gathers info and answers.
  Independent of force_plan (observe on the first message still skips).

Free flight stays reproducible by flipping both flags off.

Co-Authored-By: Claude <noreply@anthropic.com>

compressor: structured summaries, profile-aware compression, adaptive keep_recent ...

- Replace free-form summary with strict Markdown template (Goal, Active Files,
  Decisions, Completed Work, Pending Work/Todo, Errors, Key Values).
- Keep filesystem/code_exec/terminal tool results and messages with
  is_compression_critical=True verbatim during compression instead of 300-char truncation.
- Make compression profile-aware: AgentProfile gains compression_keep_recent,
  compression_max_tokens, compression_prompt_file. navi_code uses dedicated
  compression prompt and larger keep_recent/max_tokens.
- Adaptive partition_messages(): important turns (user corrections, errors,
  critical tools) survive longer; filler/social turns compress sooner.
- Increase default context_summary_max_tokens from 3000 to 4000.
- Propagate active profile changes to ContextCompressor and SubAgentRunner.

Co-Authored-By: Claude <noreply@anthropic.com>

Refactor profile tool config to explicit agent/subagent structure ...

Replaces the confusing mix of enabled_tools + mcp_servers + subagent_tools
with a single explicit structure:

    tools: {
      agent:   {native: [...], mcp: {server: [groups]}},
      subagent:{native: [...], mcp: {server: [groups]}}
    }

Why:
- Old fields mixed native and MCP names (mcp__server__tool) in one list,
  making it impossible to tell at a glance what a subagent actually gets.
- subagent_runner.py had 25 lines of runtime MCP filtering logic that
  was hard to follow and error-prone.

Changes:
- AgentProfile: add ToolConfig / ToolScopeConfig pydantic models,
  keep old fields (enabled_tools, mcp_servers, subagent_tools) for
  auto-migration via _migrate_tools validator.
- loader.py: read new "tools" key, auto-migrate legacy configs.
- agent.py: _tool_list now accepts ToolScopeConfig.
- subagent_runner.py: simplified — profile.get_subagent_tools() returns
  the exact scope, no runtime filtering needed.
- context_builder.py, list_tools.py, spawn_agent.py: updated to use
  profile.get_agent_tools() / get_subagent_tools().
- All 6 profile config.json files migrated to new schema.
- Secretary subagent now explicitly gets navi-web MCP tools for web search.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Add inherit_system_prompt and is_subagent_only mechanisms ...

- inherit_system_prompt: subagent parameter to prepend parent's system
  prompt as a base layer before subagent specialisation
- is_subagent_only: profile flag blocking switch_profile, allowing
  spawn_agent only; shown with [subagent only] tag in list_profiles
- Document both in docs/profiles.md and docs/tools.md

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Refactor MCP integration: server groups in profiles ...

- mcp_servers.json: add 'groups' (read/write/admin) for gnexus-book
- AgentProfile: new 'mcp_servers' field (server_name -> group list)
- Profile loader: parse and persist 'mcp_servers' in config.json
- Agent._tool_list(): expands mcp_servers into concrete tool names
  via McpManager.resolve_group(), wildcard '*' supported
- /agents/profiles API: includes 'mcp_servers' in response
- Profiles no longer list individual mcp_ tools in 'enabled_tools'
- discuss: gnexus-book read group
- server_admin: gnexus-book read+write+admin groups

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Multi-user auth via gnexus-auth OAuth + hybrid role/permission model ...

- Integrate gnexus-auth-client-py (GAuthClient) for OAuth flow, token refresh,
  and webhook parsing
- Add navi/auth/ package: User model, Fernet encryptor, client singleton,
  deps (get_current_user, require_admin, require_permission)
- New tables: navi_users, user_auth_sessions (auto-created on startup)
- Session/memory isolation by user_id with legacy NULL support
- Cookie-based auth proxy: /auth/login, /callback, /logout, /me
- Webhook receiver /webhooks/gnexus-auth handling user events, global logout,
  session revocation, role/permission changes
- Admin endpoints (/admin/*) gated by role + permissions
- Webclient auth store with isAdmin/hasPermission guards
- Admin-only profile filtering in /agents/profiles
- 200/200 tests passing

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

Disable thinking stalls for 3D subagents

Architecture extensibility — event bus, middleware, auto-discovery, Pydantic profiles ...

- EventBus: async pub/sub for AgentEvents, WebSocket subscribes instead of direct yield
- Declarative serialization: AgentEvent.to_wire() on all event types
- Auto-discovery for LLM backends (_discover_backends) and workers (scan navi/workers/*.py)
- AgentProfile: Pydantic BaseModel with extra='allow', @field_validator for model coercion
- Tool middleware chain: pre/post execute hooks via ToolRegistry.add_middleware()
- LoggingMiddleware: built-in, logs every tool call
- Fix pg_trgm DDL: conditional GIN indexes via DO $$ block, no CREATE EXTENSION
- New files: event_bus.py, middleware.py, logging_middleware.py

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

changed llm & new ollama param

Add structured planning review and adaptive depth

Add context providers: dynamic system message injection per LLM call ...

- navi/context_providers/ registry + built-in public_url provider (global, always injected)
- context_providers/ user directory, hot-reloaded via reload_tools
- AgentProfile.context_providers field for per-profile opt-in providers
- Agent._collect_context_injections() called before every tool-calling loop
- reload_tools now reloads both user tools and user context providers
- manuals/write_context_provider.md for Navi, docs/context_providers.md reference

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Set temperature=1.0, top_k=64, top_p=0.95 for all profiles (Google recommended for gemma4) ...

Also fixes discuss profile memory tools: use combined "memory" tool name, not nonexistent split variants.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Add per-phase planning flags and planning_mandatory ...

- planning_mandatory: disables DIRECT shortcut, forces all phases to run
- planning_phase1_enabled / phase2_enabled / phase3_enabled: per-phase toggles
- planning_phase2_enabled replaces planning_reflect_enabled (migrated in loader with backward compat)
- Migrate all profile configs; rewrite docs/profiles.md as full config reference

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Add Ollama multi-server fallback with in-memory blacklisting ...

- New FallbackOllamaBackend (navi/llm/fallback.py): tries servers and
  models in priority order; on LLMConnectionError blacklists the server
  for the process lifetime, on LLMModelNotFoundError blacklists the
  (server, model) pair — eliminates latency from repeated failed probes
- OllamaBackend now raises typed LLMConnectionError / LLMModelNotFoundError
  instead of bare LLMBackendError; accepts list[str] | str | None for model
- AgentProfile.model changed from str to list[str] (str auto-normalised);
  all profiles updated to ["gemma4:31b-cloud", "gemma4:26b-a4b-it-q4_K_M"]
- New config field OLLAMA_BACKENDS_FILE: path to [{host, api_key?}] JSON;
  when set, registry creates FallbackOllamaBackend instead of OllamaBackend
- ollama_backends.json template added (gitignored — contains API key)
- current_model ContextVar type widened to list[str] | str | None

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Use gemma4 cloud model by default

Autonomous reasoning improvements: budget, anchoring, anti-stall, validation ...

- AgentProfile: per-profile thinking mechanics flags (think_enabled,
  iteration_budget_enabled, goal_anchoring, anti_stall, step_validation,
  planning_reflect, adaptive_replan) — all profiles updated in config.json
- Iteration budget: inject remaining iterations into context so model knows
  when to wrap up; urgency levels at ≤7 and ≤3 remaining
- Goal anchoring: inject original goal + todo state every N iterations to
  prevent drift on long tasks
- Anti-stall: two signals — no todo progress for N iterations, or identical
  tool calls repeated N times; warning injected into context
- Todo step validation: marking done requires a validation field describing
  how result was verified; failed gets a soft nudge with tip for re-planning
- stream_complete: add think param to base class, ollama and openai backends
- Summarizer: raise max_tokens 1024→3000, expand system prompt with
  user-preferences section and verbatim-value instructions
- Compression card: persist to session.messages (is_compression flag on
  Message), show expandable summary in webclient with markdown body
- ToolResult.to_message_content: always include output on failure so
  tracebacks and error details reach the model (fixes silent Error: None)
- Developer profile: fix subagent profile secretary→developer, add write_tool
  to subagent_tools, clarify write_tool vs filesystem in system prompt

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Improve subagent system: isolated tools, custom prompts, context transfer, timeout ...

AgentProfile:
- New fields: subagent_tools, subagent_planning_enabled, subagent_system_prompt
- loader.py: loads subagent_tools/subagent_planning_enabled from config.json,
  reads optional subagent_system_prompt.txt per profile

Profiles:
- Each profile now has a dedicated subagent_tools list (focused subset, no admin tools)
- subagent_planning_enabled: false (configurable per profile)
- New subagent_system_prompt.txt per profile with executor-focused instructions

run_ephemeral:
- Uses profile.subagent_tools instead of enabled_tools
- Builds subagent context without persona or profiles block (focused executor)
- Injects subagent_system_prompt after profile.system_prompt
- Accepts context_transfer: priming exchange injected before task message
- Wall-clock timeout (default 5 min) checked per iteration
- Returns (result_text, completed: bool) instead of bare string
- Optionally runs planning phase if profile.subagent_planning_enabled

spawn_agent:
- Removed briefing param; task is now fully self-contained
- Added system_prompt param: custom injected prompt for this specific task
- Auto-reads parent scratchpad context_transfer section via get_section()
- Result prefixed with [STATUS: completed|limit_reached]
- Timeout 300s

scratchpad:
- Added get_section(session_id, section) helper for cross-session reads

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Add profile discoverability: list_profiles tool + system prompt injection ...

- AgentProfile: new short_description (1-line) and full_description (dict
  with specialization / when_to_use / key_tools) fields
- All 3 profile configs: structured descriptions added; list_profiles added
  to enabled_tools
- _build_system_prompt: now accepts full AgentProfile; injects compact
  "Available profiles" block into every system prompt so Navi always knows
  what other profiles exist and when to switch — dynamically, no hardcoding
- ListProfilesTool: new built-in; returns structured per-profile details
  (specialization, when_to_use, key_tools); accepts optional profile_id
  for single-profile lookup
- registry: register list_profiles_tool after profiles registry is built

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Add planning phase and scratchpad tool for smarter task execution ...

- ScratchpadTool: session-scoped working notepad with named sections
  (write/append/read/clear). Lets Navi capture intermediate findings
  between tool calls instead of losing track of them.

- Planning phase: when profile.planning_enabled=True, a fast pre-loop
  LLM call (think=False, no tools) outlines a numbered plan before
  any actions are taken. The plan is injected into session context as
  an assistant message so the model naturally continues from it.

- PlanReady event + plan_ready WebSocket message + plan card in UI
  (green-tinted, collapsible, mirroring thinking card design).

- secretary and server_admin profiles: planning_enabled=True,
  scratchpad added to enabled_tools, system prompts updated with
  explicit execution discipline instructions.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Initial implementation of the agent system core ...

- FastAPI server with REST API and WebSocket streaming
- Modular LLM backend abstraction (Ollama implemented, OpenAI stub)
- Tool system: web_search (ddgs), filesystem, http_request, code_exec, terminal
- Agent profiles: smart_home, server_admin, secretary
- Tool-calling loop with concurrent tool execution
- In-memory session store with SessionStore ABC for future persistence
- Registry pattern for tools, profiles, and backends
- Orchestrator stub as foundation for multi-agent scenarios